Python执行环境限制为Docker；建材指数看板新增历史全景图表和周期对比

建材指数监控/refresh.py

@@ -66,19 +66,131 @@ for _, r in df.tail(30).iterrows():
         'MA60': None if pd.isna(r['MA60']) else round(float(r['MA60']), 1),
     })
 
+
+def build_history(df):
+    """构建历史全景数据：月度采样 + 周期标注 + 统计。"""
+
+    # 月度采样（取每月最后一条有效记录）
+    monthly = df.set_index('日期').resample('ME')['最新值'].last().dropna().reset_index()
+    ma60_monthly = df.set_index('日期').resample('ME')['MA60'].last().dropna().reset_index()
+    monthly_vals = []
+    for _, r in monthly.iterrows():
+        ma_row = ma60_monthly[ma60_monthly['日期'] == r['日期']]
+        ma60_val = None if ma_row.empty or pd.isna(ma_row['MA60'].iloc[0]) else round(float(ma_row['MA60'].iloc[0]), 1)
+        monthly_vals.append({
+            'date': r['日期'].strftime('%Y-%m'),
+            'value': int(r['最新值']),
+            'ma60': ma60_val,
+        })
+
+    # 历史统计
+    vals = df['最新值']
+    latest_val = float(vals.iloc[-1])
+    pct_rank = round((vals < latest_val).sum() / len(vals) * 100, 1)
+
+    # 年度涨跌（排除不足 200 个交易日的首尾年）
+    df['year'] = df['日期'].dt.year
+    yearly = df.groupby('year')['最新值'].agg(['first', 'last', 'count'])
+    yearly = yearly[yearly['count'] >= 200]
+    yearly['chg'] = ((yearly['last'] - yearly['first']) / yearly['first'] * 100).round(1)
+    down_years = int((yearly['chg'] < 0).sum())
+    ytd = float(yearly['chg'].iloc[-1]) if len(yearly) > 0 else 0
+
+    # 识别主要周期：用未取整的 MA60 找极值，间距至少 4 个月
+    ma60_raw = df['最新值'].rolling(60).mean().values  # 未取整，保留精度
+    all_turns = []  # (date, value, type)
+    min_gap = 80  # 交易日，约 4 个月
+    last_turn_idx = -999
+    for i in range(2, len(ma60_raw) - 2):
+        if pd.isna(ma60_raw[i]):
+            continue
+        if ma60_raw[i] > ma60_raw[i - 1] and ma60_raw[i] > ma60_raw[i - 2] and ma60_raw[i] > ma60_raw[i + 1] and ma60_raw[i] > ma60_raw[i + 2]:
+            if i - last_turn_idx >= min_gap:
+                all_turns.append((df['日期'].iloc[i], float(ma60_raw[i]), 'peak'))
+                last_turn_idx = i
+        elif ma60_raw[i] < ma60_raw[i - 1] and ma60_raw[i] < ma60_raw[i - 2] and ma60_raw[i] < ma60_raw[i + 1] and ma60_raw[i] < ma60_raw[i + 2]:
+            if i - last_turn_idx >= min_gap:
+                all_turns.append((df['日期'].iloc[i], float(ma60_raw[i]), 'trough'))
+                last_turn_idx = i
+
+    # 构建周期（谷 → 下一个峰）
+    cycles = []
+    for i, (t_date, t_val, t_type) in enumerate(all_turns):
+        if t_type != 'trough':
+            continue
+        # 找下一个峰
+        for j in range(i + 1, len(all_turns)):
+            if all_turns[j][2] == 'peak':
+                p_date, p_val, _ = all_turns[j]
+                gain = round((p_val - t_val) / t_val * 100, 1)
+                if gain > 10:
+                    label = ''
+                    if t_date.year == 2015 or t_date.year == 2016:
+                        label = '供给侧改革'
+                    elif t_date.year == 2020:
+                        label = '疫情后刺激'
+                    cycles.append({
+                        'label': label,
+                        'start': t_date.strftime('%Y-%m'),
+                        'end': p_date.strftime('%Y-%m'),
+                        'from': int(t_val),
+                        'to': int(p_val),
+                        'pct': gain,
+                    })
+                break
+
+    # 当前反弹（用价格实际谷底，不是 MA60 谷底）
+    # 找最近 2 年的价格最低点
+    recent_2y = df[df['日期'] >= df['日期'].iloc[-1] - pd.DateOffset(years=2)]
+    trough_idx = recent_2y['最新值'].idxmin()
+    trough_date = df.loc[trough_idx, '日期']
+    trough_val = float(df.loc[trough_idx, '最新值'])
+    cur_gain = round((latest_val - trough_val) / trough_val * 100, 1)
+    current_rebound = {
+        'label': '当前反弹',
+        'start': trough_date.strftime('%Y-%m'),
+        'end': None,
+        'from': int(trough_val),
+        'to': int(latest_val),
+        'pct': cur_gain,
+    }
+
+    # 均线结构
+    last = df.iloc[-1]
+    ma20_vs_ma60 = 'above' if last['MA20'] > last['MA60'] else 'below'
+
+    return {
+        'monthly': monthly_vals,
+        'stats': {
+            'mean': round(float(vals.mean()), 1),
+            'min': int(vals.min()),
+            'max': int(vals.max()),
+            'min_date': df.loc[vals.idxmin(), '日期'].strftime('%Y-%m'),
+            'max_date': df.loc[vals.idxmax(), '日期'].strftime('%Y-%m'),
+            'percentile': pct_rank,
+            'down_years': down_years,
+            'ytd_chg': round(ytd, 1),
+        },
+        'cycles': cycles,
+        'current_rebound': current_rebound,
+        'ma20_vs_ma60': ma20_vs_ma60,
+    }
+
+
 output = {
     'summary': summary,
     'mas': mas,
     'signals': [{'period': p, 'signal': s, 'detail': d} for p, s, d in signals],
     'overall': {'signal': overall[0], 'color': overall[1]},
     'recent': rows,
+    'history': build_history(df),
 }
 
 with open('data.json', 'w') as f:
     json.dump(output, f, ensure_ascii=False, indent=2)
 
 SENTINEL = '/*__DATA_SENTINEL__*/{}'
-html = open('index.html').read()
+html = open('index_template.html').read()
 html = html.replace(SENTINEL, json.dumps(output, ensure_ascii=False))
 open('index.html', 'w').write(html)